R squared in gls regression - r

Question

I want to create a gls regression that includes the value R squared and observations where the values "log likelihood" etc. are. The p values should be below the coefficients in the table. Here is an example of a code:
`
# import the necessary packages
library(nlme)
library(dplyr)
library(stargazer)
# create a new subset that only includes observations with a value in the "Price.Book.Value" column
dotcom_subset_MBV <- dotcom_subset %>% filter(!is.na(Price.Book.Value))
financial_subset_MBV <- financial_subset %>% filter(!is.na(Price.Book.Value))
covid_subset_MBV <- covid_subset %>% filter(!is.na(Price.Book.Value))
# Hypothesis 2: Fit GLS models
dotcom_model_MBV <- gls(X1.Month.Equity.Premium ~ crisis*Price.Book.Value, data = dotcom_subset_MBV, method = "ML")
financial_model_MBV <- gls(X1.Month.Equity.Premium ~ crisis*Price.Book.Value, data = financial_subset_MBV, method = "ML")
covid_model_MBV <- gls(X1.Month.Equity.Premium ~ crisis*Price.Book.Value, data = covid_subset_MBV, method = "ML")
library(stargazer)
stargazer(dotcom_model_MBV, financial_model_MBV, covid_model_MBV, type = "text",column.labels = c("Dotcom","Financial","Covid"),report=('vc*p'))
The only problem with the code above is that it shows the Log Likelihood, Akaike Inf. Crit. and Bayesian Inf. Crit. instead of the R squared values. The rest would be okay.
I tried the following:
omit.stat = c("ll", "AIC", "BIC")
and it works. However, it still doesn't show me the R squared. Then I tried:
add.lines = list(c(paste0("R-squared = ", round(r2_dotcom, 2)
and it includes a line that is called "R Squared" but without any values.

Answer 1

Here's a working example using the mtcars data:
data(mtcars)
library(nlme)
library(stargazer)
#>
#> Please cite as:
#> Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
#> R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
m1 <- gls(qsec ~ cyl + wt, data=mtcars)
m2 <- gls(mpg ~ cyl + wt, data=mtcars)
r2 <- c(cor(fitted(m1), mtcars$qsec)^2,
cor(fitted(m2), mtcars$mpg)^2)
stargazer(m1, m2,
type="text",
omit.stat = c("ll", "AIC", "BIC"),
add.lines = list(c("R-squared", sprintf("%.2f", r2))))
#>
#> =========================================
#> Dependent variable:
#> ----------------------------
#> qsec mpg
#> (1) (2)
#> -----------------------------------------
#> cyl -1.173*** -1.508***
#> (0.197) (0.415)
#>
#> wt 1.356*** -3.191***
#> (0.360) (0.757)
#>
#> Constant 20.743*** 39.686***
#> (0.815) (1.715)
#>
#> -----------------------------------------
#> R-squared 0.56 0.83
#> Observations 32 32
#> =========================================
#> Note: *p<0.1; **p<0.05; ***p<0.01
Created on 2023-02-01 by the reprex package (v2.0.1)
The add.lines option expects a list and each vector in the list will be appended to the table with a different element in each column. So you would need the vector of values to be "R-squared" and then each of the r-squared values printed in a string (that's what sprintf() does).
Also, note, that I've calculated the r-squared as the squared correlation between the observed and fitted values, but make no claim that this is statistically sound (though it is one way of calculating R-squared in the OLS model).